About AutoInspect AI
Deep learning-powered vehicle damage detection and classification system built for production.
The Model
ResNet50 with transfer learning
AutoInspect uses a ResNet50 convolutional neural network architecture, pre-trained on ImageNet, and fine-tuned with transfer learning on a curated dataset of vehicle damage images.
The model recognizes multiple categories of vehicle damage including structural damage, glass breakage, dents, and lighting component damage. Transfer learning allows the model to leverage knowledge from millions of general images while specializing in automotive damage assessment.
The final classification layer has been replaced with a custom fully-connected head tuned for the specific damage classes, achieving strong accuracy on the validation set.
50
Layers deep
25.6M
Parameters
224×224
Input resolution
Dataset
Car damage classification training data
The model was trained on a carefully curated dataset of vehicle damage images, covering a range of damage types and severities. Each image is labeled with one of the following classification categories:
Data augmentation techniques (rotation, flipping, color jitter, random cropping) were applied during training to improve model robustness and generalization across different lighting conditions, angles, and vehicle types.
Stack
Technology Stack
Machine Learning
ResNet50
Base architecture with transfer learning from ImageNet
PyTorch / TensorFlow
Deep learning framework for model training
PIL / OpenCV
Image preprocessing and augmentation
Backend API
FastAPI
High-performance Python web framework
Uvicorn
ASGI server for production deployment
Prometheus Client
Metrics collection and exposure
Frontend
Next.js / React
Modern UI framework with server-side rendering
Tailwind CSS
Utility-first styling with custom dark theme
Recharts
Data visualization for metrics dashboard
Framer Motion
Production-grade animation library
Infrastructure
Hugging Face Spaces
ML model hosting and API deployment
Vercel
Frontend deployment with edge network
Docker
Containerized deployment environment
System Architecture
End-to-end pipeline overview